Mobile Computing, Applications, and Services. Second International ICST Conference, MobiCASE 2010, Santa Clara, CA, USA, October 25-28, 2010, Revised Selected Papers

Research Article

Dr. Droid: Assisting Stroke Rehabilitation Using Mobile Phones

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  • @INPROCEEDINGS{10.1007/978-3-642-29336-8_13,
        author={Andrew Goodney and Jinho Jung and Scott Needham and Sameera Poduri},
        title={Dr. Droid: Assisting Stroke Rehabilitation Using Mobile Phones},
        proceedings={Mobile Computing, Applications, and Services. Second International ICST Conference, MobiCASE 2010, Santa Clara, CA, USA, October 25-28, 2010, Revised Selected Papers},
        proceedings_a={MOBICASE},
        year={2012},
        month={10},
        keywords={mobile sensing gesture recognition personal health monitoring telemedicine stroke rehabilitation},
        doi={10.1007/978-3-642-29336-8_13}
    }
    
  • Andrew Goodney
    Jinho Jung
    Scott Needham
    Sameera Poduri
    Year: 2012
    Dr. Droid: Assisting Stroke Rehabilitation Using Mobile Phones
    MOBICASE
    Springer
    DOI: 10.1007/978-3-642-29336-8_13
Andrew Goodney1,*, Jinho Jung2,*, Scott Needham1,*, Sameera Poduri1,*
  • 1: University of Southern California
  • 2: KJITC
*Contact email: goodney@usc.edu, visusee@mnd.go.kr, sneedham@usc.edu, sameera@usc.edu

Abstract

In this paper we present our initial work on a mobile phone application for assisting stroke rehabilitation. We believe that using a mobile phone to administer and track stroke rehabilitation is novel. We call our system Dr. Droid and focus on the automated scoring of motions performed by patients being administered the Wolf Motor Function Test (WMFT) by placing a smart phone in a holster at the patients wrist. We have developed a complete software application that administers the test by giving audio and visual instructions. We collect a motion trace by sampling the 3-axis accelerometer available on the phone. We double-integrate the acceleration data and apply a novel reorientation algorithm to correct for mis-alignment of the accelerometer. Using dynamic time warping and hidden Markov models we assign an objective, quantitative score to the patient’s exercises. We validate our method by performing experiments designed to simulate the motions of a stroke patient.